In recent years, research has been made about object detection to detect various types of objects (such as a person's face and a car) from an image that is captured by a camera or the like. The object detection technology includes learning the features of objects to be detected to create learning data in advance, and comparing the created learning data and image data to determine whether the objects to be detected are included in the image.
The image data itself contains an enormous amount of information while the object detection technology has only to determine whether there is an object to be searched for in the image. The image data therefore needs to be reduced to save memory resources by utilizing information quantization techniques.
In some information quantization techniques, the image data is subjected to frequency conversion (wavelet transformation) and quantization processing is performed on the basis of the magnitudes of the resulting conversion coefficients (or the magnitudes of differences in pixel value between adjoining pixels) (for example, see H. Schneiderman and T. Kanade, “Object Detection Using the Statistics of Parts”, International Journal of Computer Vision, 2002, which is referred to as “Schneiderman” hereinafter). According to such quantization processing, the conversion coefficients and quantization thresholds are compared to quantize the image data in three levels. This allows a reduction of the area for storing the image data and learning data intended for object detection.
Since the chances for image data to include an object to be detected are small on the whole, comparing all the areas of the image data and those of the learning data in detail would involve comparisons of the image data and learning data even in unnecessary areas, making the object detection processing wasteful.
To avoid waste of the object detection processing, a technique has been known in which rough object detection is performed on the search areas of the image data, and detailed object detection is performed only if there is a possibility that the image data includes an object to be detected (for example, see Japanese Laid-open Patent Publication No. 2004-246618). According to such a technique, when rough object detection is performed and it is determined that the image data includes no object to be detected, the processing is shifted to the next area. This makes it possible to speed up the object detection processing.
The foregoing conventional techniques, however, have had a problem with performing the object detection with high accuracy and speeding up the object detection processing.
More specifically, according to the conventional techniques, the speedup of the object detection processing is effected by performing rough object detection on the search areas of the image data. Consequently, objects similar to but different from the object to be detected can often be mistaken for the object to be detected. After the mistaking for the object to be detected, the processing enters the detailed object detection despite the absence of the object to be detected. This consequently delays the object detection processing.